Abstract
Objective This study assessed the efficacy of machine learning in predicting thyrotoxicosis and hypothyroidism [thyroid-stimulating hormone >10.0 mIU/L] by leveraging age and sex as variables and integrating biochemical test parameters used by the Japan Society of Health Evaluation and Promotion (JHEP) and the Japan Society of Ningen Dock (JND). Methods Our study included 20,653 untreated patients with Graves' disease, 3,435 untreated patients with painless thyroiditis, 4,266 healthy individuals, and 18,937 untreated patients with Hashimoto's thyroiditis. Machine learning was conducted using Prediction One on three distinct datasets: the Ito dataset (age, sex, and 30 blood tests and biochemical test data), the JHEP dataset (age, sex, and total protein,total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γGTP), alkaline phosphatase, creatinine (CRE), uric acid (UA), and T-Cho test data), and the JND dataset (age, sex, and AST, ALT, γGTP, CRE, and UA test data). Results The results for distinguishing thyrotoxicosis patients from the healthy control group showed that the JHEP dataset yielded substantial discriminative capacity with an area under the curve (AUC) of 0.966, sensitivity of 92.2%, specificity of 89.1%, and accuracy of 91.7%. The JND dataset displayed similar robustness, with an AUC of 0.948, sensitivity of 92.0%, specificity of 81.3%, and accuracy of 90.4%. Differentiating hypothyroid patients from the healthy control group yielded similarly robust performances, with the JHEP dataset yielding AUC, sensitivity, specificity, and accuracy values of 0.864, 84.2%, 72.1%, and 77.4%, respectively, and the JND dataset yielding values of 0.840, 83.2%, 67.2%, and 74.3%, respectively. Conclusion Machine learning is a potent screening tool for thyrotoxicosis and hypothyroidism.